from dataclasses import dataclass
from typing import Any, Dict, Optional
from packaging import version
import importlib.metadata

import oneflow as torch
import oneflow.nn.functional as F
from oneflow import nn
from onediff.infer_compiler.backends.oneflow.transform import transform_mgr

transformed_diffusers = transform_mgr.transform_package("diffusers")

diffusers_0220_v = version.parse("0.22.0")
diffusers_02499_v = version.parse("0.24.99")
diffusers_0270_v = version.parse("0.27.0")
diffusers_version = version.parse(importlib.metadata.version("diffusers"))

if diffusers_version < diffusers_0220_v:
    ConfigMixin = transformed_diffusers.configuration_utils.ConfigMixin
    register_to_config = transformed_diffusers.configuration_utils.register_to_config
    ImagePositionalEmbeddings = (
        transformed_diffusers.models.embeddings.ImagePositionalEmbeddings
    )
    BaseOutput = transformed_diffusers.utils.BaseOutput
    deprecate = transformed_diffusers.utils.deprecate
    BasicTransformerBlock = transformed_diffusers.models.attention.BasicTransformerBlock
    PatchEmbed = transformed_diffusers.models.embeddings.PatchEmbed
    LoRACompatibleConv = transformed_diffusers.models.lora.LoRACompatibleConv
    LoRACompatibleLinear = transformed_diffusers.models.lora.LoRACompatibleLinear
    ModelMixin = transformed_diffusers.models.modeling_utils.ModelMixin

    @dataclass
    class Transformer2DModelOutput(BaseOutput):
        sample: torch.FloatTensor

    class Transformer2DModel(ModelMixin, ConfigMixin):
        @register_to_config
        def __init__(
            self,
            num_attention_heads: int = 16,
            attention_head_dim: int = 88,
            in_channels: Optional[int] = None,
            out_channels: Optional[int] = None,
            num_layers: int = 1,
            dropout: float = 0.0,
            norm_num_groups: int = 32,
            cross_attention_dim: Optional[int] = None,
            attention_bias: bool = False,
            sample_size: Optional[int] = None,
            num_vector_embeds: Optional[int] = None,
            patch_size: Optional[int] = None,
            activation_fn: str = "geglu",
            num_embeds_ada_norm: Optional[int] = None,
            use_linear_projection: bool = False,
            only_cross_attention: bool = False,
            upcast_attention: bool = False,
            norm_type: str = "layer_norm",
            norm_elementwise_affine: bool = True,
        ):
            super().__init__()
            self.use_linear_projection = use_linear_projection
            self.num_attention_heads = num_attention_heads
            self.attention_head_dim = attention_head_dim
            inner_dim = num_attention_heads * attention_head_dim

            # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
            # Define whether input is continuous or discrete depending on configuration
            self.is_input_continuous = (in_channels is not None) and (
                patch_size is None
            )
            self.is_input_vectorized = num_vector_embeds is not None
            self.is_input_patches = in_channels is not None and patch_size is not None

            if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
                deprecation_message = (
                    f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
                    " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
                    " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
                    " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
                    " would be very nice if you could open a Pull request for the `transformer/config.json` file"
                )
                deprecate(
                    "norm_type!=num_embeds_ada_norm",
                    "1.0.0",
                    deprecation_message,
                    standard_warn=False,
                )
                norm_type = "ada_norm"

            if self.is_input_continuous and self.is_input_vectorized:
                raise ValueError(
                    f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
                    " sure that either `in_channels` or `num_vector_embeds` is None."
                )
            elif self.is_input_vectorized and self.is_input_patches:
                raise ValueError(
                    f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
                    " sure that either `num_vector_embeds` or `num_patches` is None."
                )
            elif (
                not self.is_input_continuous
                and not self.is_input_vectorized
                and not self.is_input_patches
            ):
                raise ValueError(
                    f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
                    f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."
                )

            # 2. Define input layers
            if self.is_input_continuous:
                self.in_channels = in_channels

                self.norm = torch.nn.GroupNorm(
                    num_groups=norm_num_groups,
                    num_channels=in_channels,
                    eps=1e-6,
                    affine=True,
                )
                if use_linear_projection:
                    self.proj_in = LoRACompatibleLinear(in_channels, inner_dim)
                else:
                    self.proj_in = LoRACompatibleConv(
                        in_channels, inner_dim, kernel_size=1, stride=1, padding=0
                    )
            elif self.is_input_vectorized:
                assert (
                    sample_size is not None
                ), "Transformer2DModel over discrete input must provide sample_size"
                assert (
                    num_vector_embeds is not None
                ), "Transformer2DModel over discrete input must provide num_embed"

                self.height = sample_size
                self.width = sample_size
                self.num_vector_embeds = num_vector_embeds
                self.num_latent_pixels = self.height * self.width

                self.latent_image_embedding = ImagePositionalEmbeddings(
                    num_embed=num_vector_embeds,
                    embed_dim=inner_dim,
                    height=self.height,
                    width=self.width,
                )
            elif self.is_input_patches:
                assert (
                    sample_size is not None
                ), "Transformer2DModel over patched input must provide sample_size"

                self.height = sample_size
                self.width = sample_size

                self.patch_size = patch_size
                self.pos_embed = PatchEmbed(
                    height=sample_size,
                    width=sample_size,
                    patch_size=patch_size,
                    in_channels=in_channels,
                    embed_dim=inner_dim,
                )

            # 3. Define transformers blocks
            self.transformer_blocks = nn.ModuleList(
                [
                    BasicTransformerBlock(
                        inner_dim,
                        num_attention_heads,
                        attention_head_dim,
                        dropout=dropout,
                        cross_attention_dim=cross_attention_dim,
                        activation_fn=activation_fn,
                        num_embeds_ada_norm=num_embeds_ada_norm,
                        attention_bias=attention_bias,
                        only_cross_attention=only_cross_attention,
                        upcast_attention=upcast_attention,
                        norm_type=norm_type,
                        norm_elementwise_affine=norm_elementwise_affine,
                    )
                    for d in range(num_layers)
                ]
            )

            # 4. Define output layers
            self.out_channels = in_channels if out_channels is None else out_channels
            if self.is_input_continuous:
                # TODO: should use out_channels for continuous projections
                if use_linear_projection:
                    self.proj_out = LoRACompatibleLinear(inner_dim, in_channels)
                else:
                    self.proj_out = LoRACompatibleConv(
                        inner_dim, in_channels, kernel_size=1, stride=1, padding=0
                    )
            elif self.is_input_vectorized:
                self.norm_out = nn.LayerNorm(inner_dim)
                self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1)
            elif self.is_input_patches:
                self.norm_out = nn.LayerNorm(
                    inner_dim, elementwise_affine=False, eps=1e-6
                )
                self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim)
                self.proj_out_2 = nn.Linear(
                    inner_dim, patch_size * patch_size * self.out_channels
                )

        def forward(
            self,
            hidden_states: torch.Tensor,
            encoder_hidden_states: Optional[torch.Tensor] = None,
            timestep: Optional[torch.LongTensor] = None,
            class_labels: Optional[torch.LongTensor] = None,
            cross_attention_kwargs: Dict[str, Any] = None,
            attention_mask: Optional[torch.Tensor] = None,
            encoder_attention_mask: Optional[torch.Tensor] = None,
            return_dict: bool = True,
        ):
            """
            The [`Transformer2DModel`] forward method.

            Args:
                hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
                    Input `hidden_states`.
                encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
                    Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
                    self-attention.
                timestep ( `torch.LongTensor`, *optional*):
                    Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
                class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
                    Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
                    `AdaLayerZeroNorm`.
                encoder_attention_mask ( `torch.Tensor`, *optional*):
                    Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:

                        * Mask `(batch, sequence_length)` True = keep, False = discard.
                        * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.

                    If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
                    above. This bias will be added to the cross-attention scores.
                return_dict (`bool`, *optional*, defaults to `True`):
                    Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
                    tuple.

            Returns:
                If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
                `tuple` where the first element is the sample tensor.
            """
            # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
            #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
            #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
            # expects mask of shape:
            #   [batch, key_tokens]
            # adds singleton query_tokens dimension:
            #   [batch,                    1, key_tokens]
            # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
            #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
            #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
            if attention_mask is not None and attention_mask.ndim == 2:
                # assume that mask is expressed as:
                #   (1 = keep,      0 = discard)
                # convert mask into a bias that can be added to attention scores:
                #       (keep = +0,     discard = -10000.0)
                attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
                attention_mask = attention_mask.unsqueeze(1)

            # convert encoder_attention_mask to a bias the same way we do for attention_mask
            if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
                encoder_attention_mask = (
                    1 - encoder_attention_mask.to(hidden_states.dtype)
                ) * -10000.0
                encoder_attention_mask = encoder_attention_mask.unsqueeze(1)

            hidden_states_in = hidden_states
            # 1. Input
            if self.is_input_continuous:
                batch, _, height, width = hidden_states.shape
                residual = hidden_states

                hidden_states = self.norm(hidden_states)
                if not self.use_linear_projection:
                    hidden_states = self.proj_in(hidden_states)
                    inner_dim = hidden_states.shape[1]
                    # modified to support dynamic shape for onediff
                    # hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
                    hidden_states = hidden_states.permute(0, 2, 3, 1).flatten(1, 2)
                else:
                    inner_dim = hidden_states.shape[1]
                    # modified to support dynamic shape for onediff
                    # hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
                    hidden_states = hidden_states.permute(0, 2, 3, 1).flatten(1, 2)
                    hidden_states = self.proj_in(hidden_states)
            elif self.is_input_vectorized:
                hidden_states = self.latent_image_embedding(hidden_states)
            elif self.is_input_patches:
                hidden_states = self.pos_embed(hidden_states)

            # 2. Blocks
            for block in self.transformer_blocks:
                hidden_states = block(
                    hidden_states,
                    attention_mask=attention_mask,
                    encoder_hidden_states=encoder_hidden_states,
                    encoder_attention_mask=encoder_attention_mask,
                    timestep=timestep,
                    cross_attention_kwargs=cross_attention_kwargs,
                    class_labels=class_labels,
                )

            # 3. Output
            if self.is_input_continuous:
                if not self.use_linear_projection:
                    # hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
                    hidden_states = (
                        hidden_states.permute(0, 2, 1)
                        .reshape_as(hidden_states_in)
                        .contiguous()
                    )
                    hidden_states = self.proj_out(hidden_states)
                else:
                    hidden_states = self.proj_out(hidden_states)
                    # hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
                    hidden_states = (
                        hidden_states.permute(0, 2, 1)
                        .reshape_as(hidden_states_in)
                        .contiguous()
                    )

                output = hidden_states + residual
            elif self.is_input_vectorized:
                hidden_states = self.norm_out(hidden_states)
                logits = self.out(hidden_states)
                # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
                logits = logits.permute(0, 2, 1)

                # log(p(x_0))
                output = F.log_softmax(logits.double(), dim=1).float()
            elif self.is_input_patches:
                # TODO: cleanup!
                conditioning = self.transformer_blocks[0].norm1.emb(
                    timestep, class_labels, hidden_dtype=hidden_states.dtype
                )
                shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
                hidden_states = (
                    self.norm_out(hidden_states) * (1 + scale[:, None]) + shift[:, None]
                )
                hidden_states = self.proj_out_2(hidden_states)

                # unpatchify
                height = width = int(hidden_states.shape[1] ** 0.5)
                hidden_states = hidden_states.reshape(
                    shape=(
                        -1,
                        height,
                        width,
                        self.patch_size,
                        self.patch_size,
                        self.out_channels,
                    )
                )
                hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
                output = hidden_states.reshape(
                    shape=(
                        -1,
                        self.out_channels,
                        height * self.patch_size,
                        width * self.patch_size,
                    )
                )

            if not return_dict:
                return (output,)

            return Transformer2DModelOutput(sample=output)


elif diffusers_version < diffusers_02499_v:
    ConfigMixin = transformed_diffusers.configuration_utils.ConfigMixin
    register_to_config = transformed_diffusers.configuration_utils.register_to_config
    ImagePositionalEmbeddings = (
        transformed_diffusers.models.embeddings.ImagePositionalEmbeddings
    )
    USE_PEFT_BACKEND = transformed_diffusers.utils.USE_PEFT_BACKEND
    BaseOutput = transformed_diffusers.utils.BaseOutput
    deprecate = transformed_diffusers.utils.deprecate
    BasicTransformerBlock = transformed_diffusers.models.attention.BasicTransformerBlock
    CaptionProjection = transformed_diffusers.models.embeddings.CaptionProjection
    PatchEmbed = transformed_diffusers.models.embeddings.PatchEmbed
    LoRACompatibleConv = transformed_diffusers.models.lora.LoRACompatibleConv
    LoRACompatibleLinear = transformed_diffusers.models.lora.LoRACompatibleLinear
    ModelMixin = transformed_diffusers.models.modeling_utils.ModelMixin
    AdaLayerNormSingle = transformed_diffusers.models.normalization.AdaLayerNormSingle

    @dataclass
    class Transformer2DModelOutput(BaseOutput):
        """
        The output of [`Transformer2DModel`].

        Args:
            sample (`torch.FloatTensor` of shape `(batch_size, num_channels, height, width)` or `(batch size, num_vector_embeds - 1, num_latent_pixels)` if [`Transformer2DModel`] is discrete):
                The hidden states output conditioned on the `encoder_hidden_states` input. If discrete, returns probability
                distributions for the unnoised latent pixels.
        """

        sample: torch.FloatTensor

    class Transformer2DModel(ModelMixin, ConfigMixin):
        """
        A 2D Transformer model for image-like data.

        Parameters:
            num_attention_heads (`int`, *optional*, defaults to 16): The number of heads to use for multi-head attention.
            attention_head_dim (`int`, *optional*, defaults to 88): The number of channels in each head.
            in_channels (`int`, *optional*):
                The number of channels in the input and output (specify if the input is **continuous**).
            num_layers (`int`, *optional*, defaults to 1): The number of layers of Transformer blocks to use.
            dropout (`float`, *optional*, defaults to 0.0): The dropout probability to use.
            cross_attention_dim (`int`, *optional*): The number of `encoder_hidden_states` dimensions to use.
            sample_size (`int`, *optional*): The width of the latent images (specify if the input is **discrete**).
                This is fixed during training since it is used to learn a number of position embeddings.
            num_vector_embeds (`int`, *optional*):
                The number of classes of the vector embeddings of the latent pixels (specify if the input is **discrete**).
                Includes the class for the masked latent pixel.
            activation_fn (`str`, *optional*, defaults to `"geglu"`): Activation function to use in feed-forward.
            num_embeds_ada_norm ( `int`, *optional*):
                The number of diffusion steps used during training. Pass if at least one of the norm_layers is
                `AdaLayerNorm`. This is fixed during training since it is used to learn a number of embeddings that are
                added to the hidden states.

                During inference, you can denoise for up to but not more steps than `num_embeds_ada_norm`.
            attention_bias (`bool`, *optional*):
                Configure if the `TransformerBlocks` attention should contain a bias parameter.
        """

        @register_to_config
        def __init__(
            self,
            num_attention_heads: int = 16,
            attention_head_dim: int = 88,
            in_channels: Optional[int] = None,
            out_channels: Optional[int] = None,
            num_layers: int = 1,
            dropout: float = 0.0,
            norm_num_groups: int = 32,
            cross_attention_dim: Optional[int] = None,
            attention_bias: bool = False,
            sample_size: Optional[int] = None,
            num_vector_embeds: Optional[int] = None,
            patch_size: Optional[int] = None,
            activation_fn: str = "geglu",
            num_embeds_ada_norm: Optional[int] = None,
            use_linear_projection: bool = False,
            only_cross_attention: bool = False,
            double_self_attention: bool = False,
            upcast_attention: bool = False,
            norm_type: str = "layer_norm",
            norm_elementwise_affine: bool = True,
            norm_eps: float = 1e-5,
            attention_type: str = "default",
            caption_channels: int = None,
        ):
            super().__init__()
            self.use_linear_projection = use_linear_projection
            self.num_attention_heads = num_attention_heads
            self.attention_head_dim = attention_head_dim
            inner_dim = num_attention_heads * attention_head_dim

            conv_cls = nn.Conv2d if USE_PEFT_BACKEND else LoRACompatibleConv
            linear_cls = nn.Linear if USE_PEFT_BACKEND else LoRACompatibleLinear

            # 1. Transformer2DModel can process both standard continuous images of shape `(batch_size, num_channels, width, height)` as well as quantized image embeddings of shape `(batch_size, num_image_vectors)`
            # Define whether input is continuous or discrete depending on configuration
            self.is_input_continuous = (in_channels is not None) and (
                patch_size is None
            )
            self.is_input_vectorized = num_vector_embeds is not None
            self.is_input_patches = in_channels is not None and patch_size is not None

            if norm_type == "layer_norm" and num_embeds_ada_norm is not None:
                deprecation_message = (
                    f"The configuration file of this model: {self.__class__} is outdated. `norm_type` is either not set or"
                    " incorrectly set to `'layer_norm'`.Make sure to set `norm_type` to `'ada_norm'` in the config."
                    " Please make sure to update the config accordingly as leaving `norm_type` might led to incorrect"
                    " results in future versions. If you have downloaded this checkpoint from the Hugging Face Hub, it"
                    " would be very nice if you could open a Pull request for the `transformer/config.json` file"
                )
                deprecate(
                    "norm_type!=num_embeds_ada_norm",
                    "1.0.0",
                    deprecation_message,
                    standard_warn=False,
                )
                norm_type = "ada_norm"

            if self.is_input_continuous and self.is_input_vectorized:
                raise ValueError(
                    f"Cannot define both `in_channels`: {in_channels} and `num_vector_embeds`: {num_vector_embeds}. Make"
                    " sure that either `in_channels` or `num_vector_embeds` is None."
                )
            elif self.is_input_vectorized and self.is_input_patches:
                raise ValueError(
                    f"Cannot define both `num_vector_embeds`: {num_vector_embeds} and `patch_size`: {patch_size}. Make"
                    " sure that either `num_vector_embeds` or `num_patches` is None."
                )
            elif (
                not self.is_input_continuous
                and not self.is_input_vectorized
                and not self.is_input_patches
            ):
                raise ValueError(
                    f"Has to define `in_channels`: {in_channels}, `num_vector_embeds`: {num_vector_embeds}, or patch_size:"
                    f" {patch_size}. Make sure that `in_channels`, `num_vector_embeds` or `num_patches` is not None."
                )

            # 2. Define input layers
            if self.is_input_continuous:
                self.in_channels = in_channels

                self.norm = torch.nn.GroupNorm(
                    num_groups=norm_num_groups,
                    num_channels=in_channels,
                    eps=1e-6,
                    affine=True,
                )
                if use_linear_projection:
                    self.proj_in = linear_cls(in_channels, inner_dim)
                else:
                    self.proj_in = conv_cls(
                        in_channels, inner_dim, kernel_size=1, stride=1, padding=0
                    )
            elif self.is_input_vectorized:
                assert (
                    sample_size is not None
                ), "Transformer2DModel over discrete input must provide sample_size"
                assert (
                    num_vector_embeds is not None
                ), "Transformer2DModel over discrete input must provide num_embed"

                self.height = sample_size
                self.width = sample_size
                self.num_vector_embeds = num_vector_embeds
                self.num_latent_pixels = self.height * self.width

                self.latent_image_embedding = ImagePositionalEmbeddings(
                    num_embed=num_vector_embeds,
                    embed_dim=inner_dim,
                    height=self.height,
                    width=self.width,
                )
            elif self.is_input_patches:
                assert (
                    sample_size is not None
                ), "Transformer2DModel over patched input must provide sample_size"

                self.height = sample_size
                self.width = sample_size

                self.patch_size = patch_size
                interpolation_scale = (
                    self.config.sample_size // 64
                )  # => 64 (= 512 pixart) has interpolation scale 1
                interpolation_scale = max(interpolation_scale, 1)
                self.pos_embed = PatchEmbed(
                    height=sample_size,
                    width=sample_size,
                    patch_size=patch_size,
                    in_channels=in_channels,
                    embed_dim=inner_dim,
                    interpolation_scale=interpolation_scale,
                )

            # 3. Define transformers blocks
            self.transformer_blocks = nn.ModuleList(
                [
                    BasicTransformerBlock(
                        inner_dim,
                        num_attention_heads,
                        attention_head_dim,
                        dropout=dropout,
                        cross_attention_dim=cross_attention_dim,
                        activation_fn=activation_fn,
                        num_embeds_ada_norm=num_embeds_ada_norm,
                        attention_bias=attention_bias,
                        only_cross_attention=only_cross_attention,
                        double_self_attention=double_self_attention,
                        upcast_attention=upcast_attention,
                        norm_type=norm_type,
                        norm_elementwise_affine=norm_elementwise_affine,
                        norm_eps=norm_eps,
                        attention_type=attention_type,
                    )
                    for d in range(num_layers)
                ]
            )

            # 4. Define output layers
            self.out_channels = in_channels if out_channels is None else out_channels
            if self.is_input_continuous:
                # TODO: should use out_channels for continuous projections
                if use_linear_projection:
                    self.proj_out = linear_cls(inner_dim, in_channels)
                else:
                    self.proj_out = conv_cls(
                        inner_dim, in_channels, kernel_size=1, stride=1, padding=0
                    )
            elif self.is_input_vectorized:
                self.norm_out = nn.LayerNorm(inner_dim)
                self.out = nn.Linear(inner_dim, self.num_vector_embeds - 1)
            elif self.is_input_patches and norm_type != "ada_norm_single":
                self.norm_out = nn.LayerNorm(
                    inner_dim, elementwise_affine=False, eps=1e-6
                )
                self.proj_out_1 = nn.Linear(inner_dim, 2 * inner_dim)
                self.proj_out_2 = nn.Linear(
                    inner_dim, patch_size * patch_size * self.out_channels
                )
            elif self.is_input_patches and norm_type == "ada_norm_single":
                self.norm_out = nn.LayerNorm(
                    inner_dim, elementwise_affine=False, eps=1e-6
                )
                self.scale_shift_table = nn.Parameter(
                    torch.randn(2, inner_dim) / inner_dim ** 0.5
                )
                self.proj_out = nn.Linear(
                    inner_dim, patch_size * patch_size * self.out_channels
                )

            # 5. PixArt-Alpha blocks.
            self.adaln_single = None
            self.use_additional_conditions = False
            if norm_type == "ada_norm_single":
                self.use_additional_conditions = self.config.sample_size == 128
                # TODO(Sayak, PVP) clean this, for now we use sample size to determine whether to use
                # additional conditions until we find better name
                self.adaln_single = AdaLayerNormSingle(
                    inner_dim, use_additional_conditions=self.use_additional_conditions
                )

            self.caption_projection = None
            if caption_channels is not None:
                self.caption_projection = CaptionProjection(
                    in_features=caption_channels, hidden_size=inner_dim
                )

            self.gradient_checkpointing = False

        def forward(
            self,
            hidden_states: torch.Tensor,
            encoder_hidden_states: Optional[torch.Tensor] = None,
            timestep: Optional[torch.LongTensor] = None,
            added_cond_kwargs: Dict[str, torch.Tensor] = None,
            class_labels: Optional[torch.LongTensor] = None,
            cross_attention_kwargs: Dict[str, Any] = None,
            attention_mask: Optional[torch.Tensor] = None,
            encoder_attention_mask: Optional[torch.Tensor] = None,
            return_dict: bool = True,
        ):
            """
            The [`Transformer2DModel`] forward method.

            Args:
                hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
                    Input `hidden_states`.
                encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
                    Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
                    self-attention.
                timestep ( `torch.LongTensor`, *optional*):
                    Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
                class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
                    Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
                    `AdaLayerZeroNorm`.
                cross_attention_kwargs ( `Dict[str, Any]`, *optional*):
                    A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                    `self.processor` in
                    [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
                attention_mask ( `torch.Tensor`, *optional*):
                    An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
                    is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
                    negative values to the attention scores corresponding to "discard" tokens.
                encoder_attention_mask ( `torch.Tensor`, *optional*):
                    Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:

                        * Mask `(batch, sequence_length)` True = keep, False = discard.
                        * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.

                    If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
                    above. This bias will be added to the cross-attention scores.
                return_dict (`bool`, *optional*, defaults to `True`):
                    Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
                    tuple.

            Returns:
                If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
                `tuple` where the first element is the sample tensor.
            """
            # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
            #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
            #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
            # expects mask of shape:
            #   [batch, key_tokens]
            # adds singleton query_tokens dimension:
            #   [batch,                    1, key_tokens]
            # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
            #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
            #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
            if attention_mask is not None and attention_mask.ndim == 2:
                # assume that mask is expressed as:
                #   (1 = keep,      0 = discard)
                # convert mask into a bias that can be added to attention scores:
                #       (keep = +0,     discard = -10000.0)
                attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
                attention_mask = attention_mask.unsqueeze(1)

            # convert encoder_attention_mask to a bias the same way we do for attention_mask
            if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
                encoder_attention_mask = (
                    1 - encoder_attention_mask.to(hidden_states.dtype)
                ) * -10000.0
                encoder_attention_mask = encoder_attention_mask.unsqueeze(1)

            # Retrieve lora scale.
            lora_scale = (
                cross_attention_kwargs.get("scale", 1.0)
                if cross_attention_kwargs is not None
                else 1.0
            )

            hidden_states_in = hidden_states
            # 1. Input
            if self.is_input_continuous:
                batch, _, height, width = hidden_states.shape
                residual = hidden_states

                hidden_states = self.norm(hidden_states)
                if not self.use_linear_projection:
                    hidden_states = (
                        self.proj_in(hidden_states, scale=lora_scale)
                        if not USE_PEFT_BACKEND
                        else self.proj_in(hidden_states)
                    )
                    inner_dim = hidden_states.shape[1]
                    # modified to support dynamic shape for onediff
                    # hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
                    hidden_states = hidden_states.permute(0, 2, 3, 1).flatten(1, 2)
                else:
                    inner_dim = hidden_states.shape[1]
                    # modified to support dynamic shape for onediff
                    # hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
                    hidden_states = hidden_states.permute(0, 2, 3, 1).flatten(1, 2)
                    hidden_states = (
                        self.proj_in(hidden_states, scale=lora_scale)
                        if not USE_PEFT_BACKEND
                        else self.proj_in(hidden_states)
                    )

            elif self.is_input_vectorized:
                hidden_states = self.latent_image_embedding(hidden_states)
            elif self.is_input_patches:
                hidden_states = self.pos_embed(hidden_states)

                if self.adaln_single is not None:
                    if self.use_additional_conditions and added_cond_kwargs is None:
                        raise ValueError(
                            "`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`."
                        )
                    batch_size = hidden_states.shape[0]
                    timestep, embedded_timestep = self.adaln_single(
                        timestep,
                        added_cond_kwargs,
                        batch_size=batch_size,
                        hidden_dtype=hidden_states.dtype,
                    )

            # 2. Blocks
            if self.caption_projection is not None:
                batch_size = hidden_states.shape[0]
                encoder_hidden_states = self.caption_projection(encoder_hidden_states)
                encoder_hidden_states = encoder_hidden_states.view(
                    batch_size, -1, hidden_states.shape[-1]
                )

            for block in self.transformer_blocks:
                if self.training and self.gradient_checkpointing:
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        block,
                        hidden_states,
                        attention_mask,
                        encoder_hidden_states,
                        encoder_attention_mask,
                        timestep,
                        cross_attention_kwargs,
                        class_labels,
                        use_reentrant=False,
                    )
                else:
                    hidden_states = block(
                        hidden_states,
                        attention_mask=attention_mask,
                        encoder_hidden_states=encoder_hidden_states,
                        encoder_attention_mask=encoder_attention_mask,
                        timestep=timestep,
                        cross_attention_kwargs=cross_attention_kwargs,
                        class_labels=class_labels,
                    )

            # 3. Output
            if self.is_input_continuous:
                if not self.use_linear_projection:
                    # hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
                    hidden_states = (
                        hidden_states.permute(0, 2, 1)
                        .reshape_as(hidden_states_in)
                        .contiguous()
                    )
                    hidden_states = (
                        self.proj_out(hidden_states, scale=lora_scale)
                        if not USE_PEFT_BACKEND
                        else self.proj_out(hidden_states)
                    )
                else:
                    hidden_states = (
                        self.proj_out(hidden_states, scale=lora_scale)
                        if not USE_PEFT_BACKEND
                        else self.proj_out(hidden_states)
                    )
                    # hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
                    hidden_states = (
                        hidden_states.permute(0, 2, 1)
                        .reshape_as(hidden_states_in)
                        .contiguous()
                    )

                output = hidden_states + residual
            elif self.is_input_vectorized:
                hidden_states = self.norm_out(hidden_states)
                logits = self.out(hidden_states)
                # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
                logits = logits.permute(0, 2, 1)

                # log(p(x_0))
                output = F.log_softmax(logits.double(), dim=1).float()

            if self.is_input_patches:
                if self.config.norm_type != "ada_norm_single":
                    conditioning = self.transformer_blocks[0].norm1.emb(
                        timestep, class_labels, hidden_dtype=hidden_states.dtype
                    )
                    shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
                    hidden_states = (
                        self.norm_out(hidden_states) * (1 + scale[:, None])
                        + shift[:, None]
                    )
                    hidden_states = self.proj_out_2(hidden_states)
                elif self.config.norm_type == "ada_norm_single":
                    shift, scale = (
                        self.scale_shift_table[None] + embedded_timestep[:, None]
                    ).chunk(2, dim=1)
                    hidden_states = self.norm_out(hidden_states)
                    # Modulation
                    hidden_states = hidden_states * (1 + scale) + shift
                    hidden_states = self.proj_out(hidden_states)
                    hidden_states = hidden_states.squeeze(1)

                # unpatchify
                height = width = int(hidden_states.shape[1] ** 0.5)
                hidden_states = hidden_states.reshape(
                    shape=(
                        -1,
                        height,
                        width,
                        self.patch_size,
                        self.patch_size,
                        self.out_channels,
                    )
                )
                hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
                output = hidden_states.reshape(
                    shape=(
                        -1,
                        self.out_channels,
                        height * self.patch_size,
                        width * self.patch_size,
                    )
                )

            if not return_dict:
                return (output,)

            return Transformer2DModelOutput(sample=output)


else:
    transformed_diffusers = transform_mgr.transform_package("diffusers")
    ConfigMixin = transformed_diffusers.configuration_utils.ConfigMixin
    register_to_config = transformed_diffusers.configuration_utils.register_to_config
    ImagePositionalEmbeddings = (
        transformed_diffusers.models.embeddings.ImagePositionalEmbeddings
    )
    USE_PEFT_BACKEND = transformed_diffusers.utils.USE_PEFT_BACKEND
    BaseOutput = transformed_diffusers.utils.BaseOutput
    deprecate = transformed_diffusers.utils.deprecate
    is_torch_version = transformed_diffusers.utils.is_torch_version
    BasicTransformerBlock = transformed_diffusers.models.attention.BasicTransformerBlock
    PatchEmbed = transformed_diffusers.models.embeddings.PatchEmbed
    PixArtAlphaTextProjection = (
        transformed_diffusers.models.embeddings.PixArtAlphaTextProjection
    )
    LoRACompatibleConv = transformed_diffusers.models.lora.LoRACompatibleConv
    LoRACompatibleLinear = transformed_diffusers.models.lora.LoRACompatibleLinear
    ModelMixin = transformed_diffusers.models.modeling_utils.ModelMixin
    AdaLayerNormSingle = transformed_diffusers.models.normalization.AdaLayerNormSingle
    Transformer2DModelOutput = (
        transformed_diffusers.models.transformer_2d.Transformer2DModelOutput
    )
    proxy_Transformer2DModel = (
        transformed_diffusers.models.transformer_2d.Transformer2DModel
    )

    class Transformer2DModel(proxy_Transformer2DModel):
        def forward(
            self,
            hidden_states: torch.Tensor,
            encoder_hidden_states: Optional[torch.Tensor] = None,
            timestep: Optional[torch.LongTensor] = None,
            added_cond_kwargs: Dict[str, torch.Tensor] = None,
            class_labels: Optional[torch.LongTensor] = None,
            cross_attention_kwargs: Dict[str, Any] = None,
            attention_mask: Optional[torch.Tensor] = None,
            encoder_attention_mask: Optional[torch.Tensor] = None,
            return_dict: bool = True,
        ):
            """
            The [`Transformer2DModel`] forward method.

            Args:
                hidden_states (`torch.LongTensor` of shape `(batch size, num latent pixels)` if discrete, `torch.FloatTensor` of shape `(batch size, channel, height, width)` if continuous):
                    Input `hidden_states`.
                encoder_hidden_states ( `torch.FloatTensor` of shape `(batch size, sequence len, embed dims)`, *optional*):
                    Conditional embeddings for cross attention layer. If not given, cross-attention defaults to
                    self-attention.
                timestep ( `torch.LongTensor`, *optional*):
                    Used to indicate denoising step. Optional timestep to be applied as an embedding in `AdaLayerNorm`.
                class_labels ( `torch.LongTensor` of shape `(batch size, num classes)`, *optional*):
                    Used to indicate class labels conditioning. Optional class labels to be applied as an embedding in
                    `AdaLayerZeroNorm`.
                cross_attention_kwargs ( `Dict[str, Any]`, *optional*):
                    A kwargs dictionary that if specified is passed along to the `AttentionProcessor` as defined under
                    `self.processor` in
                    [diffusers.models.attention_processor](https://github.com/huggingface/diffusers/blob/main/src/diffusers/models/attention_processor.py).
                attention_mask ( `torch.Tensor`, *optional*):
                    An attention mask of shape `(batch, key_tokens)` is applied to `encoder_hidden_states`. If `1` the mask
                    is kept, otherwise if `0` it is discarded. Mask will be converted into a bias, which adds large
                    negative values to the attention scores corresponding to "discard" tokens.
                encoder_attention_mask ( `torch.Tensor`, *optional*):
                    Cross-attention mask applied to `encoder_hidden_states`. Two formats supported:

                        * Mask `(batch, sequence_length)` True = keep, False = discard.
                        * Bias `(batch, 1, sequence_length)` 0 = keep, -10000 = discard.

                    If `ndim == 2`: will be interpreted as a mask, then converted into a bias consistent with the format
                    above. This bias will be added to the cross-attention scores.
                return_dict (`bool`, *optional*, defaults to `True`):
                    Whether or not to return a [`~models.unet_2d_condition.UNet2DConditionOutput`] instead of a plain
                    tuple.

            Returns:
                If `return_dict` is True, an [`~models.transformer_2d.Transformer2DModelOutput`] is returned, otherwise a
                `tuple` where the first element is the sample tensor.
            """
            if diffusers_version >= diffusers_0270_v:
                if cross_attention_kwargs is not None:
                    if cross_attention_kwargs.get("scale", None) is not None:
                        logger.warning(
                            "Passing `scale` to `cross_attention_kwargs` is depcrecated. `scale` will be ignored."
                        )
            # ensure attention_mask is a bias, and give it a singleton query_tokens dimension.
            #   we may have done this conversion already, e.g. if we came here via UNet2DConditionModel#forward.
            #   we can tell by counting dims; if ndim == 2: it's a mask rather than a bias.
            # expects mask of shape:
            #   [batch, key_tokens]
            # adds singleton query_tokens dimension:
            #   [batch,                    1, key_tokens]
            # this helps to broadcast it as a bias over attention scores, which will be in one of the following shapes:
            #   [batch,  heads, query_tokens, key_tokens] (e.g. torch sdp attn)
            #   [batch * heads, query_tokens, key_tokens] (e.g. xformers or classic attn)
            if attention_mask is not None and attention_mask.ndim == 2:
                # assume that mask is expressed as:
                #   (1 = keep,      0 = discard)
                # convert mask into a bias that can be added to attention scores:
                #       (keep = +0,     discard = -10000.0)
                attention_mask = (1 - attention_mask.to(hidden_states.dtype)) * -10000.0
                attention_mask = attention_mask.unsqueeze(1)

            # convert encoder_attention_mask to a bias the same way we do for attention_mask
            if encoder_attention_mask is not None and encoder_attention_mask.ndim == 2:
                encoder_attention_mask = (
                    1 - encoder_attention_mask.to(hidden_states.dtype)
                ) * -10000.0
                encoder_attention_mask = encoder_attention_mask.unsqueeze(1)

            if diffusers_version < diffusers_0270_v:
                # Retrieve lora scale.
                lora_scale = (
                    cross_attention_kwargs.get("scale", 1.0)
                    if cross_attention_kwargs is not None
                    else 1.0
                )

            hidden_states_in = hidden_states
            # 1. Input
            if self.is_input_continuous:
                batch, _, height, width = hidden_states.shape
                residual = hidden_states

                hidden_states = self.norm(hidden_states)
                if not self.use_linear_projection:
                    if diffusers_version < diffusers_0270_v:
                        hidden_states = (
                            self.proj_in(hidden_states, scale=lora_scale)
                            if not USE_PEFT_BACKEND
                            else self.proj_in(hidden_states)
                        )
                    else:
                        hidden_states = self.proj_in(hidden_states)
                    inner_dim = hidden_states.shape[1]
                    # modified to support dynamic shape for onediff
                    # hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
                    hidden_states = hidden_states.permute(0, 2, 3, 1).flatten(1, 2)
                else:
                    inner_dim = hidden_states.shape[1]
                    # modified to support dynamic shape for onediff
                    # hidden_states = hidden_states.permute(0, 2, 3, 1).reshape(batch, height * width, inner_dim)
                    hidden_states = hidden_states.permute(0, 2, 3, 1).flatten(1, 2)
                    if diffusers_version < diffusers_0270_v:
                        hidden_states = (
                            self.proj_in(hidden_states, scale=lora_scale)
                            if not USE_PEFT_BACKEND
                            else self.proj_in(hidden_states)
                        )
                    else:
                        hidden_states = self.proj_in(hidden_states)

            elif self.is_input_vectorized:
                hidden_states = self.latent_image_embedding(hidden_states)
            elif self.is_input_patches:
                height, width = (
                    hidden_states.shape[-2] // self.patch_size,
                    hidden_states.shape[-1] // self.patch_size,
                )
                hidden_states = self.pos_embed(hidden_states)

                if self.adaln_single is not None:
                    if self.use_additional_conditions and added_cond_kwargs is None:
                        raise ValueError(
                            "`added_cond_kwargs` cannot be None when using additional conditions for `adaln_single`."
                        )
                    batch_size = hidden_states.shape[0]
                    timestep, embedded_timestep = self.adaln_single(
                        timestep,
                        added_cond_kwargs,
                        batch_size=batch_size,
                        hidden_dtype=hidden_states.dtype,
                    )

            # 2. Blocks
            if self.caption_projection is not None:
                batch_size = hidden_states.shape[0]
                encoder_hidden_states = self.caption_projection(encoder_hidden_states)
                encoder_hidden_states = encoder_hidden_states.view(
                    batch_size, -1, hidden_states.shape[-1]
                )

            for block in self.transformer_blocks:
                if self.training and self.gradient_checkpointing:

                    def create_custom_forward(module, return_dict=None):
                        def custom_forward(*inputs):
                            if return_dict is not None:
                                return module(*inputs, return_dict=return_dict)
                            else:
                                return module(*inputs)

                        return custom_forward

                    ckpt_kwargs: Dict[str, Any] = {
                        "use_reentrant": False
                    } if is_torch_version(">=", "1.11.0") else {}
                    hidden_states = torch.utils.checkpoint.checkpoint(
                        create_custom_forward(block),
                        hidden_states,
                        attention_mask,
                        encoder_hidden_states,
                        encoder_attention_mask,
                        timestep,
                        cross_attention_kwargs,
                        class_labels,
                        **ckpt_kwargs,
                    )
                else:
                    hidden_states = block(
                        hidden_states,
                        attention_mask=attention_mask,
                        encoder_hidden_states=encoder_hidden_states,
                        encoder_attention_mask=encoder_attention_mask,
                        timestep=timestep,
                        cross_attention_kwargs=cross_attention_kwargs,
                        class_labels=class_labels,
                    )

            # 3. Output
            if self.is_input_continuous:
                if not self.use_linear_projection:
                    # hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
                    hidden_states = (
                        hidden_states.permute(0, 2, 1)
                        .reshape_as(hidden_states_in)
                        .contiguous()
                    )
                    if diffusers_version < diffusers_0270_v:
                        hidden_states = (
                            self.proj_out(hidden_states, scale=lora_scale)
                            if not USE_PEFT_BACKEND
                            else self.proj_out(hidden_states)
                        )
                    else:
                        hidden_states = self.proj_out(hidden_states)
                else:
                    if diffusers_version < diffusers_0270_v:
                        hidden_states = (
                            self.proj_out(hidden_states, scale=lora_scale)
                            if not USE_PEFT_BACKEND
                            else self.proj_out(hidden_states)
                        )
                    else:
                        hidden_states = self.proj_out(hidden_states)
                    # hidden_states = hidden_states.reshape(batch, height, width, inner_dim).permute(0, 3, 1, 2).contiguous()
                    hidden_states = (
                        hidden_states.permute(0, 2, 1)
                        .reshape_as(hidden_states_in)
                        .contiguous()
                    )

                output = hidden_states + residual
            elif self.is_input_vectorized:
                hidden_states = self.norm_out(hidden_states)
                logits = self.out(hidden_states)
                # (batch, self.num_vector_embeds - 1, self.num_latent_pixels)
                logits = logits.permute(0, 2, 1)

                # log(p(x_0))
                output = F.log_softmax(logits.double(), dim=1).float()

            if self.is_input_patches:
                if self.config.norm_type != "ada_norm_single":
                    conditioning = self.transformer_blocks[0].norm1.emb(
                        timestep, class_labels, hidden_dtype=hidden_states.dtype
                    )
                    shift, scale = self.proj_out_1(F.silu(conditioning)).chunk(2, dim=1)
                    hidden_states = (
                        self.norm_out(hidden_states) * (1 + scale[:, None])
                        + shift[:, None]
                    )
                    hidden_states = self.proj_out_2(hidden_states)
                elif self.config.norm_type == "ada_norm_single":
                    shift, scale = (
                        self.scale_shift_table[None] + embedded_timestep[:, None]
                    ).chunk(2, dim=1)
                    hidden_states = self.norm_out(hidden_states)
                    # Modulation
                    hidden_states = hidden_states * (1 + scale) + shift
                    hidden_states = self.proj_out(hidden_states)
                    hidden_states = hidden_states.squeeze(1)

                # unpatchify
                if self.adaln_single is None:
                    height = width = int(hidden_states.shape[1] ** 0.5)
                hidden_states = hidden_states.reshape(
                    shape=(
                        -1,
                        height,
                        width,
                        self.patch_size,
                        self.patch_size,
                        self.out_channels,
                    )
                )
                hidden_states = torch.einsum("nhwpqc->nchpwq", hidden_states)
                output = hidden_states.reshape(
                    shape=(
                        -1,
                        self.out_channels,
                        height * self.patch_size,
                        width * self.patch_size,
                    )
                )

            if not return_dict:
                return (output,)

            return Transformer2DModelOutput(sample=output)
